Project Name
AI-Assisted Build Pipeline Optimisation at Enterprise Scale
The client is a large-scale enterprise technology organisation operating across multiple geographies, managing a complex distributed infrastructure estate built up over more than a decade. Their automation and build engineering teams maintained thousands of Ansible playbooks, shell scripts, and Perl-based workflows that had grown organically with no standardisation, documentation, or performance oversight.
Build cycles that should have taken minutes were routinely consuming 8 to 12 hours, inefficient scripts were burning 3 to 5 times the necessary compute, and the fragility of the legacy estate meant engineers were too risk-averse to modernise it. Delivery velocity was suffering, and the build pipeline had become a hard ceiling on engineering throughput across the entire organisation.
Ksolves, an AI-First company applied AI-assisted script analysis and modern pipeline engineering to identify bottlenecks, rewrite critical path components, and re-architect the CI/CD platform end to end. The result was an approximately 70% reduction in build times, a significant drop in compute resource consumption, and a fully maintainable automation estate that scales with the organisation’s growing engineering teams.
The challenges faced by the client are as follows:
- Build Times Consuming 8 to 12 Hours: Legacy Perl and shell script workflows executed sequentially with no parallelisation, causing builds that modern pipelines would complete in under 2 hours to routinely exceed a full working day.
- Resource-Heavy Executions: Inefficient scripts consumed 3 to 5 times more CPU and memory than optimised equivalents, inflating cloud compute costs and constraining the number of concurrent pipeline runs the platform could support.
- Unmaintainable Script Library: Thousands of scripts had been written by engineers who had since left the organisation, with minimal documentation, no standard patterns, and heavy use of deprecated language constructs.
- No Centralised Visibility: Build failure root causes were opaque. The absence of structured logging and observability meant engineers spent hours debugging failures that structured pipelines would have surfaced in minutes.
- Change Risk Blocking Modernisation: The fragility of the legacy estate meant any modernisation attempt carried high regression risk, discouraging the engineering teams from initiating the refactoring the platform clearly required.
- CI/CD Scalability Ceiling: The existing pipeline architecture could not scale to support the organisation's growing delivery teams, creating a throughput ceiling that became more visible with each new team onboarded.
Ksolves collaborated with the organisation to design and deliver an AI-assisted script analysis and pipeline modernisation programme. The engagement analysed the entire automation estate, identified optimisation opportunities, rewrote critical path components, and re-architected the CI/CD pipeline with parallelisation, intelligent caching, and structured observability, all without introducing regression risk.
- AI-Powered Script Analysis Engine: Applied static analysis and AI-driven pattern recognition across the full script library to identify dead code, redundant dependencies, sequential bottlenecks, and modernisation candidates, producing a prioritised optimisation roadmap before a single line was rewritten.
- Perl-to-Python and Ansible Refactoring: Rewrote critical path scripts into maintainable Python and optimised Ansible roles, applying idempotency, modular design, and modern error handling throughout the rewritten estate.
- Pipeline Parallelisation Architecture: Re-architected Jenkins pipelines to GitHub Actions with parallel job execution, stage-level dependency mapping, and intelligent job splitting, eliminating the serial execution bottlenecks at the root of the build time problem.
- Intelligent Caching Layer: Implemented dependency caching at the artifact, container image, and test result levels, ensuring unchanged components did not re-execute on every build. This was the single highest-impact optimisation for build time reduction.
- Structured Observability Integration: Embedded structured logging, build telemetry, and failure analytics across all pipelines, giving engineering teams immediate root cause visibility and eliminating the hours previously spent debugging opaque failures.
Technology Stack
| Category | Technology |
|---|---|
| DevSecOps | GitHub Actions |
| Infrastructure | Ansible (Optimised Roles) |
| AI/ML | Python + AST Script Analysis |
| Processing | Python 3.x |
| Platform | GitHub Actions Cache |
- 70% Reduction in Build Time: Builds averaging 8 to 12 hours across legacy sequential pipelines now complete equivalent workloads in 2 to 3 hours via parallelised GitHub Actions with intelligent caching.
- Significant Compute Cost Reduction: Inefficient scripts that were consuming 3 to 5 times optimal compute on every run were replaced with right-sized executions, achieving materially lower resource consumption per build.
- Script Maintainability Restored: Thousands of undocumented, deprecated-language scripts that no current engineer fully understood were replaced with a rewritten Python and Ansible estate featuring consistent patterns, documentation, and structure maintainable by any team member.
- Build Failure Diagnosis Time Reduced to Minutes: Structured observability now surfaces root cause at first glance, reducing mean time to resolution from hours of manual log trawling to minutes.
- CI/CD Throughput Scalability Achieved: The parallelised architecture scales linearly with team growth without infrastructure changes, eliminating the throughput ceiling that had been constraining delivery velocity.
By integrating AI-driven script analysis, Perl-to-Python refactoring, pipeline parallelisation, and intelligent caching, Ksolves transformed the client’s build estate end to end. Build times fell by approximately 70%, compute utilisation dropped significantly, and the full automation library is now maintainable and ready to scale.
If your build pipeline is holding back your engineering teams, Ksolves AI and ML Consulting Services can help you modernise it, cut compute costs, and eliminate maintenance debt across your entire automation estate.
Is your build pipeline still running on legacy scripts that were never designed to scale?
